> h2o.init () # Import the Iris (with headers) dataset. > pkgs for ( pkg in pkgs ) # Run the following command to load the H2O: > library ( h2o ) # Run the following command to initialize H2O on your local machine (single-node cluster) using all available CPUs. # Copy and paste the following commands in R to download dependency packages. # Follow our security guidelines () # if you want to secure your installation. Type 'demo()' for some demos, 'help()' for on - line help, or 'help.start()' for an HTML browser interface to help. When the download is complete, unzip the file and install. Depending on your OS, download the appropriate file, along with any required packages. Select your operating system (Linux, OS X, or Windows). GitHub Help: The GitHub Help system is a useful resource for becoming familiar with Git. Launch from the command line: This document describes some of the additional options that you can configure when launching H2O (for example, to specify a different directory for saved Flow data, to allocate more memory, or to use a flatfile for quick configuration of a cluster).Īlgorithms: This section describes the science behind our algorithms and provides a detailed, per-algo view of each model type. This interface is similar to IPython notebooks, and allows you to create a visual workflow to share with others. Using Flow - H2O’s Web UI: This section describes our new intuitive web interface, Flow. Tutorials: To see a step-by-step example of our algorithms in action, select a model type from the following list: Make sure to install Java if it is not already installed. If you’re just getting started with H2O, here are some links to help youĭownloads page: First things first - download a copy of H2O here by selecting a build under “Download H2O” (the “Bleeding Edge” build contains the latest changes, while the latest alpha release is a more stable build), then use the installation instruction tabs to install H2O on your client of choice (standalone, R, Python, Hadoop, or Maven).įor first-time users, we recommend downloading the latest alpha release and the default standalone option (the first tab) as the installation method. H2O is licensed under the Apache License, Version 2.0. The speed, quality, ease-of-use, and model-deployment for the various cutting edge Supervised and Unsupervised algorithms like Deep Learning, Tree Ensembles, and GLRM make H2O a highly sought after API for big data data science. The Rest API is used by H2O’s web interface (Flow UI), R binding (H2O-R), and Python binding (H2O-Python). H2O’s REST API allows access to all the capabilities of H2O from an external program or script via JSON over HTTP. H2O’s data parser has built-in intelligence to guess the schema of the incoming dataset and supports data ingest from multiple sources in various formats. The data is read in parallel and is distributed across the cluster and stored in memory in a columnar format in a compressed way. The algorithms are implemented on top of H2O’s distributed Map/Reduce framework and utilize the Java Fork/Join framework for multi-threading. Inside H2O, a Distributed Key/Value store is used to access and reference data, models, objects, etc., across all nodes and machines. H2O is an open source, in-memory, distributed, fast, and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data and provides easy productionalization of those models in an enterprise environment. Saving, Loading, Downloading, and Uploading Models.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |